Anthropometrics are a set of direct quantitative measurements of the human body’s external dimensions, which can be used as indirect measures of body composition. Due to a number of limitations of conventional manual techniques for the collection of body measurements, advanced systems using three-dimensional (3D) scanners are currently being employed, despite being a relatively new technique. A systematic review was carried out using Pubmed, Medline and the Cochrane Library to assess whether 3D scanners offer reproducible, reliable and accurate data with respect to anthropometrics. Although significant differences were found, 3D measurements correlated strongly with measurements made by conventional anthropometry, dual-energy X-ray absorptiometry (DXA) and air displacement plethysmography (ADP), among others. In most studies (61.1%), 3D scanners were more accurate than these other techniques; in fact, these scanners presented excellent accuracy or reliability. 3D scanners allow automated, quick and easy measurements of different body tissues. Moreover, they seem to provide reproducible, reliable and accurate data that correlate well with the other techniques used.

The exponential growth of the Internet of Things (IoT) necessitates robust, scalable, and secure identity management solutions to handle the vast number of interconnected devices. Traditional centralized identity systems are increasingly inadequate due to their vulnerabilities, such as single points of failure, scalability issues, and limited user control over data. This study explores a decentralized identity management model leveraging the IOTA Tangle, a Directed Acyclic Graph (DAG)-based distributed ledger technology, to address these challenges. By integrating Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and IOTA-specific technologies like IOTA Identity, IOTA Streams, and IOTA Stronghold, we propose a proof-of-concept framework that enhances security, scalability, and privacy in IoT ecosystems. Our implementation on resource-constrained IoT devices demonstrates the feasibility of this approach, highlighting significant improvements in transaction efficiency, real-time data exchange, and cryptographic key management. Furthermore, this research aligns with Web 3.0 principles, emphasizing decentralization, user autonomy, and data sovereignty. The findings suggest that IOTA-based solutions can effectively advance secure and user-centric identity management in IoT, paving the way for broader applications in various domains, including smart cities and healthcare.

The overexploitation of seas and oceans is a major problem that affects the world's fisheries. One of the main consequences is the loss of marine biodiversity that affects not only the ecosystem themselves but also the fishing industries. Nowadays, the fishing sector is immersed in a crucial process of digitization, as it is a traditional sector that lacks, among other things, complete, accurate, and reliable information on fish catches. Being able to know the species that arrive at fish markets can give stakeholders a detailed picture of the general health of the fisheries, as well as that of the populations of particular species of interest, to take appropriate actions. This paper presents an automated monitoring system of fish catches in fish markets based on computer vision and deep learning methods. Specifically, the system can identify instances of fish species based on Yolact models. Experiments have been performed comparing different network backbone architectures using the newly introduced DeepFish dataset for direct in-tray recognition,

Public security is a crucial aspect of maintaining social order. Although crime rates in Western cultures may be considered socially acceptable, it is important to continually improve security measures to prevent potential risks. With the advancements in artificial intelligence methods, particularly in deep learning and computer vision, it has become possible to detect abnormal event patterns in groups of people. This paper presents a review of the deep learning techniques employed for identifying gatherings of people and detecting anomalous events to enhance public security. Some of the open research areas are identified, including the lack of works addressing multiple cases of anomalies in large concentrations of people, which leaves open an important avenue for future scientific work.